Overview

Dataset statistics

Number of variables21
Number of observations21714
Missing cells35814
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory168.0 B

Variable types

NUM11
CAT10

Warnings

property_type has constant value "21714" Constant
country has constant value "21714" Constant
city has constant value "21714" Constant
current_zones has a high cardinality: 595 distinct values High cardinality
zone has a high cardinality: 419 distinct values High cardinality
century_zone has a high cardinality: 141 distinct values High cardinality
property_status has 1342 (6.2%) missing values Missing
current_zones has 1057 (4.9%) missing values Missing
zone has 1057 (4.9%) missing values Missing
century_zone has 2052 (9.5%) missing values Missing
other_rooms has 2450 (11.3%) missing values Missing
year_of_construction has 3594 (16.6%) missing values Missing
year_of_renovation has 3595 (16.6%) missing values Missing
closed_price has 20656 (95.1%) missing values Missing
price is highly skewed (γ1 = 64.5158093) Skewed
interior_area is highly skewed (γ1 = 30.34516288) Skewed
gros_area is highly skewed (γ1 = 139.8331842) Skewed
year_of_construction is highly skewed (γ1 = 105.0771713) Skewed
year_of_renovation is highly skewed (γ1 = 21.21459507) Skewed
df_index has unique values Unique
price has 697 (3.2%) zeros Zeros
interior_area has 8375 (38.6%) zeros Zeros
gros_area has 2903 (13.4%) zeros Zeros
bedrooms has 1713 (7.9%) zeros Zeros
bathrooms has 1994 (9.2%) zeros Zeros
other_rooms has 17999 (82.9%) zeros Zeros
year_of_construction has 16114 (74.2%) zeros Zeros
year_of_renovation has 18079 (83.3%) zeros Zeros

Reproduction

Analysis started2021-05-25 17:39:00.123820
Analysis finished2021-05-25 17:39:22.113093
Duration21.99 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct21714
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10857.26292
Minimum0
Maximum21736
Zeros1
Zeros (%)< 0.1%
Memory size169.6 KiB
2021-05-25T19:39:22.256317image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1085.65
Q15428.25
median10856.5
Q316284.75
95-th percentile20630.35
Maximum21736
Range21736
Interquartile range (IQR)10856.5

Descriptive statistics

Standard deviation6269.707704
Coefficient of variation (CV)0.5774666923
Kurtosis-1.199202011
Mean10857.26292
Median Absolute Deviation (MAD)5428.5
Skewness0.0006495971522
Sum235754607
Variance39309234.7
MonotocityStrictly increasing
2021-05-25T19:39:22.443139image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
211511< 0.1%
 
149941< 0.1%
 
129471< 0.1%
 
27081< 0.1%
 
6611< 0.1%
 
68061< 0.1%
 
47591< 0.1%
 
191001< 0.1%
 
170531< 0.1%
 
Other values (21704)21704> 99.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
217361< 0.1%
 
217351< 0.1%
 
217341< 0.1%
 
217331< 0.1%
 
217321< 0.1%
 

propertiesid
Real number (ℝ≥0)

Distinct20844
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209069.6781
Minimum2645
Maximum948301
Zeros0
Zeros (%)0.0%
Memory size169.6 KiB
2021-05-25T19:39:22.639218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2645
5-th percentile4401.65
Q113089.25
median87979
Q3386907.75
95-th percentile847437.25
Maximum948301
Range945656
Interquartile range (IQR)373818.5

Descriptive statistics

Standard deviation280007.4863
Coefficient of variation (CV)1.339302231
Kurtosis0.2908881723
Mean209069.6781
Median Absolute Deviation (MAD)77778.5
Skewness1.302112205
Sum4539738991
Variance7.840419236e+10
MonotocityNot monotonic
2021-05-25T19:39:22.814605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
89703< 0.1%
 
96163< 0.1%
 
136163< 0.1%
 
98083< 0.1%
 
96023< 0.1%
 
109223< 0.1%
 
109323< 0.1%
 
93523< 0.1%
 
90803< 0.1%
 
89723< 0.1%
 
Other values (20834)2168499.9%
 
ValueCountFrequency (%) 
26451< 0.1%
 
26481< 0.1%
 
26491< 0.1%
 
26501< 0.1%
 
26511< 0.1%
 
ValueCountFrequency (%) 
9483011< 0.1%
 
9482751< 0.1%
 
9482191< 0.1%
 
9478611< 0.1%
 
9475961< 0.1%
 

property_type
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.6 KiB
Apartment
21714 
ValueCountFrequency (%) 
Apartment21714100.0%
 
2021-05-25T19:39:22.994263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-25T19:39:23.114032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:23.181707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length9
Min length9

property_status
Categorical

MISSING

Distinct9
Distinct (%)< 0.1%
Missing1342
Missing (%)6.2%
Memory size169.6 KiB
Used
12210 
New
7382 
Under Construction
 
748
In project
 
11
Remodelled
 
6
Other values (4)
 
15
ValueCountFrequency (%) 
Used1221056.2%
 
New738234.0%
 
Under Construction7483.4%
 
In project110.1%
 
Remodelled6< 0.1%
 
Not Applicable5< 0.1%
 
Refurbished5< 0.1%
 
For refurbishment4< 0.1%
 
To demolish or rebuild1< 0.1%
 
(Missing)13426.2%
 
2021-05-25T19:39:23.343357image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-05-25T19:39:23.479544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:23.634711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length4
Mean length4.092336741
Min length3

availability
Categorical

Distinct8
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size169.6 KiB
Withdrawn
13139 
Sold
4181 
Available
3929 
In evaluation
 
300
Reserved
 
84
Other values (3)
 
80
ValueCountFrequency (%) 
Withdrawn1313960.5%
 
Sold418119.3%
 
Available392918.1%
 
In evaluation3001.4%
 
Reserved840.4%
 
Rented630.3%
 
In negotiation160.1%
 
Potential1< 0.1%
 
(Missing)1< 0.1%
 
2021-05-25T19:39:23.812942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-05-25T19:39:23.944510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:24.080488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length9
Mean length8.08335636
Min length3

country
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.6 KiB
Albania
21714 
ValueCountFrequency (%) 
Albania21714100.0%
 
2021-05-25T19:39:24.223144image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-25T19:39:24.292965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:24.358753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length7
Min length7

division
Categorical

Distinct5
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size169.6 KiB
Tirana
21699 
Durres
 
5
Budva
 
1
Berat
 
1
Elbasan
 
1
ValueCountFrequency (%) 
Tirana2169999.9%
 
Durres5< 0.1%
 
Budva1< 0.1%
 
Berat1< 0.1%
 
Elbasan1< 0.1%
 
(Missing)7< 0.1%
 
2021-05-25T19:39:24.467138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2021-05-25T19:39:24.546005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:24.654258image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length6
Mean length5.998986829
Min length3

city
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.6 KiB
Tirana
21714 
ValueCountFrequency (%) 
Tirana21714100.0%
 
2021-05-25T19:39:24.768616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-25T19:39:24.838820image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:24.904289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

current_zones
Categorical

HIGH CARDINALITY
MISSING

Distinct595
Distinct (%)2.9%
Missing1057
Missing (%)4.9%
Memory size169.6 KiB
Fresku
 
1509
Unaza e re
 
1116
Komuna e Parisit
 
723
Rruga e Kavajes
 
502
Kodra e Diellit
 
502
Other values (590)
16305 
ValueCountFrequency (%) 
Fresku15096.9%
 
Unaza e re11165.1%
 
Komuna e Parisit7233.3%
 
Rruga e Kavajes5022.3%
 
Kodra e Diellit5022.3%
 
Astiri4992.3%
 
21 Dhjetori4682.2%
 
Don Bosko4131.9%
 
Ali Demi4111.9%
 
Liqeni i Thatë3771.7%
 
Other values (585)1413765.1%
 
(Missing)10574.9%
 
2021-05-25T19:39:25.041760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique251 ?
Unique (%)1.2%
2021-05-25T19:39:25.210214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length96
Median length11
Mean length12.3328728
Min length3

zone
Categorical

HIGH CARDINALITY
MISSING

Distinct419
Distinct (%)2.0%
Missing1057
Missing (%)4.9%
Memory size169.6 KiB
Fresku
 
1510
Unaza e re
 
1149
Komuna e Parisit
 
729
Rruga e Kavajes
 
510
Astiri
 
509
Other values (414)
16250 
ValueCountFrequency (%) 
Fresku15107.0%
 
Unaza e re11495.3%
 
Komuna e Parisit7293.4%
 
Rruga e Kavajes5102.3%
 
Astiri5092.3%
 
21 Dhjetori4682.2%
 
Don Bosko4201.9%
 
Ali Demi4171.9%
 
Liqeni i Thatë3781.7%
 
Kodra e Diellit3761.7%
 
Other values (409)1419165.4%
 
(Missing)10574.9%
 
2021-05-25T19:39:25.378760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique108 ?
Unique (%)0.5%
2021-05-25T19:39:25.537753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length45
Median length11
Mean length12.01188174
Min length2

century_zone
Categorical

HIGH CARDINALITY
MISSING

Distinct141
Distinct (%)0.7%
Missing2052
Missing (%)9.5%
Memory size169.6 KiB
Fresku
1555 
Unaza e Re
 
1234
Don Bosco
 
864
21 Dhjetori
 
820
Ali Demi
 
754
Other values (136)
14435 
ValueCountFrequency (%) 
Fresku15557.2%
 
Unaza e Re12345.7%
 
Don Bosco8644.0%
 
21 Dhjetori8203.8%
 
Ali Demi7543.5%
 
Komuna e Parisit7293.4%
 
Liqeni i Thatë7053.2%
 
Astiri5442.5%
 
Rruga e Kavajes5112.4%
 
Yzberish5092.3%
 
Other values (131)1143752.7%
 
(Missing)20529.5%
 
2021-05-25T19:39:25.698772image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique12 ?
Unique (%)0.1%
2021-05-25T19:39:25.855682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length37
Median length10
Mean length11.36423506
Min length3

price
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct2493
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95521.109
Minimum0
Maximum14087000
Zeros697
Zeros (%)3.2%
Memory size169.6 KiB
2021-05-25T19:39:25.993948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.65
Q157000
median79900
Q3113000
95-th percentile210740
Maximum14087000
Range14087000
Interquartile range (IQR)56000

Descriptive statistics

Standard deviation127400.214
Coefficient of variation (CV)1.333738849
Kurtosis6768.538573
Mean95521.109
Median Absolute Deviation (MAD)25100
Skewness64.5158093
Sum2074145361
Variance1.623081452e+10
MonotocityNot monotonic
2021-05-25T19:39:26.141780image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
06973.2%
 
650004802.2%
 
750004522.1%
 
850004111.9%
 
550003881.8%
 
700003801.8%
 
600003781.7%
 
800003771.7%
 
900003211.5%
 
1000003031.4%
 
Other values (2483)1752780.7%
 
ValueCountFrequency (%) 
06973.2%
 
15< 0.1%
 
81< 0.1%
 
102< 0.1%
 
191< 0.1%
 
ValueCountFrequency (%) 
140870001< 0.1%
 
38236481< 0.1%
 
32000001< 0.1%
 
25000001< 0.1%
 
24000001< 0.1%
 

interior_area
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct290
Distinct (%)1.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean54.61027956
Minimum0
Maximum5700
Zeros8375
Zeros (%)38.6%
Memory size169.6 KiB
2021-05-25T19:39:26.295652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median60
Q394
95-th percentile135
Maximum5700
Range5700
Interquartile range (IQR)94

Descriptive statistics

Standard deviation87.29917109
Coefficient of variation (CV)1.598584951
Kurtosis1621.855475
Mean54.61027956
Median Absolute Deviation (MAD)58
Skewness30.34516288
Sum1185753
Variance7621.145274
MonotocityNot monotonic
2021-05-25T19:39:26.433468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0837538.6%
 
26683.1%
 
1002821.3%
 
12661.2%
 
902601.2%
 
952221.0%
 
802111.0%
 
942091.0%
 
752071.0%
 
1102071.0%
 
Other values (280)1080649.8%
 
ValueCountFrequency (%) 
0837538.6%
 
12661.2%
 
26683.1%
 
31730.8%
 
4140.1%
 
ValueCountFrequency (%) 
57001< 0.1%
 
50001< 0.1%
 
37461< 0.1%
 
36001< 0.1%
 
27771< 0.1%
 

gros_area
Real number (ℝ)

SKEWED
ZEROS

Distinct341
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.87100488
Minimum-2
Maximum166600
Zeros2903
Zeros (%)13.4%
Memory size169.6 KiB
2021-05-25T19:39:26.581265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q161
median90
Q3112
95-th percentile156
Maximum166600
Range166602
Interquartile range (IQR)51

Descriptive statistics

Standard deviation1151.229364
Coefficient of variation (CV)12.13468083
Kurtosis20163.39047
Mean94.87100488
Median Absolute Deviation (MAD)25
Skewness139.8331842
Sum2060029
Variance1325329.048
MonotocityNot monotonic
2021-05-25T19:39:26.717905image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0290313.4%
 
16112.8%
 
24922.3%
 
1004702.2%
 
1103911.8%
 
1053851.8%
 
753251.5%
 
703231.5%
 
1203141.4%
 
902971.4%
 
Other values (331)1520370.0%
 
ValueCountFrequency (%) 
-24< 0.1%
 
0290313.4%
 
16112.8%
 
24922.3%
 
310< 0.1%
 
ValueCountFrequency (%) 
1666001< 0.1%
 
200001< 0.1%
 
190001< 0.1%
 
76001< 0.1%
 
64501< 0.1%
 

bedrooms
Real number (ℝ≥0)

ZEROS

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.742470296
Minimum0
Maximum21
Zeros1713
Zeros (%)7.9%
Memory size169.6 KiB
2021-05-25T19:39:26.841967image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8658430802
Coefficient of variation (CV)0.4969055039
Kurtosis16.99153747
Mean1.742470296
Median Absolute Deviation (MAD)0
Skewness0.9846865158
Sum37836
Variance0.7496842395
MonotocityNot monotonic
2021-05-25T19:39:26.941749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
21141352.6%
 
1558325.7%
 
3275012.7%
 
017137.9%
 
42000.9%
 
5250.1%
 
6110.1%
 
117< 0.1%
 
86< 0.1%
 
73< 0.1%
 
Other values (3)3< 0.1%
 
ValueCountFrequency (%) 
017137.9%
 
1558325.7%
 
21141352.6%
 
3275012.7%
 
42000.9%
 
ValueCountFrequency (%) 
211< 0.1%
 
117< 0.1%
 
101< 0.1%
 
91< 0.1%
 
86< 0.1%
 

bathrooms
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.299926308
Minimum0
Maximum52
Zeros1994
Zeros (%)9.2%
Memory size169.6 KiB
2021-05-25T19:39:27.048974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile2
Maximum52
Range52
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7517924134
Coefficient of variation (CV)0.57833464
Kurtosis974.5832978
Mean1.299926308
Median Absolute Deviation (MAD)0
Skewness15.07399576
Sum28224
Variance0.5651918328
MonotocityNot monotonic
2021-05-25T19:39:27.152305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
11156353.3%
 
2793636.5%
 
019949.2%
 
31790.8%
 
4260.1%
 
67< 0.1%
 
52< 0.1%
 
72< 0.1%
 
211< 0.1%
 
521< 0.1%
 
(Missing)2< 0.1%
 
ValueCountFrequency (%) 
019949.2%
 
11156353.3%
 
2793636.5%
 
31790.8%
 
4260.1%
 
ValueCountFrequency (%) 
521< 0.1%
 
211< 0.1%
 
91< 0.1%
 
72< 0.1%
 
67< 0.1%
 

other_rooms
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing2450
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean0.09203696013
Minimum0
Maximum6
Zeros17999
Zeros (%)82.9%
Memory size169.6 KiB
2021-05-25T19:39:27.250454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4032146734
Coefficient of variation (CV)4.381008161
Kurtosis45.43580949
Mean0.09203696013
Median Absolute Deviation (MAD)0
Skewness5.953485465
Sum1773
Variance0.1625820729
MonotocityNot monotonic
2021-05-25T19:39:27.345324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
01799982.9%
 
19434.3%
 
21840.8%
 
31050.5%
 
4220.1%
 
57< 0.1%
 
64< 0.1%
 
(Missing)245011.3%
 
ValueCountFrequency (%) 
01799982.9%
 
19434.3%
 
21840.8%
 
31050.5%
 
4220.1%
 
ValueCountFrequency (%) 
64< 0.1%
 
57< 0.1%
 
4220.1%
 
31050.5%
 
21840.8%
 

year_of_construction
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct62
Distinct (%)0.3%
Missing3594
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean232.4990618
Minimum0
Maximum199636
Zeros16114
Zeros (%)74.2%
Memory size169.6 KiB
2021-05-25T19:39:27.506575image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2016
Maximum199636
Range199636
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1609.666125
Coefficient of variation (CV)6.923323099
Kurtosis12998.78477
Mean232.4990618
Median Absolute Deviation (MAD)0
Skewness105.0771713
Sum4212883
Variance2591025.034
MonotocityNot monotonic
2021-05-25T19:39:27.678405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01611474.2%
 
20214081.9%
 
20202781.3%
 
20101790.8%
 
20051010.5%
 
2019910.4%
 
2008720.3%
 
2000620.3%
 
2012610.3%
 
2015590.3%
 
Other values (52)6953.2%
 
(Missing)359416.6%
 
ValueCountFrequency (%) 
01611474.2%
 
13< 0.1%
 
23< 0.1%
 
701< 0.1%
 
851< 0.1%
 
ValueCountFrequency (%) 
1996361< 0.1%
 
20241< 0.1%
 
20232< 0.1%
 
2022380.2%
 
20214081.9%
 

year_of_renovation
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct13
Distinct (%)0.1%
Missing3595
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean4.451570175
Minimum0
Maximum2021
Zeros18079
Zeros (%)83.3%
Memory size169.6 KiB
2021-05-25T19:39:27.813934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2021
Range2021
Interquartile range (IQR)0

Descriptive statistics

Standard deviation94.64193047
Coefficient of variation (CV)21.26034787
Kurtosis448.1109438
Mean4.451570175
Median Absolute Deviation (MAD)0
Skewness21.21459507
Sum80658
Variance8957.095002
MonotocityNot monotonic
2021-05-25T19:39:27.922064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
01807983.3%
 
2019110.1%
 
20186< 0.1%
 
20215< 0.1%
 
20174< 0.1%
 
20154< 0.1%
 
20103< 0.1%
 
20202< 0.1%
 
20161< 0.1%
 
20091< 0.1%
 
Other values (3)3< 0.1%
 
(Missing)359516.6%
 
ValueCountFrequency (%) 
01807983.3%
 
20001< 0.1%
 
20051< 0.1%
 
20081< 0.1%
 
20091< 0.1%
 
ValueCountFrequency (%) 
20215< 0.1%
 
20202< 0.1%
 
2019110.1%
 
20186< 0.1%
 
20174< 0.1%
 

closed_price
Real number (ℝ≥0)

MISSING

Distinct260
Distinct (%)24.6%
Missing20656
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean77390.3724
Minimum150
Maximum888000
Zeros0
Zeros (%)0.0%
Memory size169.6 KiB
2021-05-25T19:39:28.064370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile34000
Q151000
median65000
Q387000
95-th percentile145750
Maximum888000
Range887850
Interquartile range (IQR)36000

Descriptive statistics

Standard deviation59664.72297
Coefficient of variation (CV)0.7709579515
Kurtosis70.73878212
Mean77390.3724
Median Absolute Deviation (MAD)17000
Skewness6.612967691
Sum81879014
Variance3559879167
MonotocityNot monotonic
2021-05-25T19:39:28.590301image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
60000280.1%
 
65000270.1%
 
70000260.1%
 
45000230.1%
 
53000210.1%
 
67000200.1%
 
80000180.1%
 
55000180.1%
 
40000180.1%
 
56000170.1%
 
Other values (250)8423.9%
 
(Missing)2065695.1%
 
ValueCountFrequency (%) 
1501< 0.1%
 
2301< 0.1%
 
2402< 0.1%
 
2501< 0.1%
 
2801< 0.1%
 
ValueCountFrequency (%) 
8880001< 0.1%
 
8709891< 0.1%
 
5300001< 0.1%
 
4700002< 0.1%
 
4350001< 0.1%
 

agency
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.6 KiB
century
10000 
futurehome
9023 
elite
1392 
mei
1299 
ValueCountFrequency (%) 
century1000046.1%
 
futurehome902341.6%
 
elite13926.4%
 
mei12996.0%
 
2021-05-25T19:39:28.723589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-25T19:39:28.800659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:28.901060image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length7
Mean length7.879110251
Min length3

Interactions

2021-05-25T19:39:04.684282image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:04.828114image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:04.950159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:05.074883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:05.198376image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:05.327606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-05-25T19:39:05.586665image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:05.719661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:05.853086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:05.986438image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:06.107844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:06.225057image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-05-25T19:39:06.445400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:06.555630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:06.672124image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:06.792878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:06.906725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:07.026946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:07.147844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:07.268481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:07.376557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:07.498095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:07.610440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:07.724471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:07.838169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:07.957864image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:08.086005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:08.203610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:08.326934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-05-25T19:39:08.578746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:08.691334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:08.813092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:08.925116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:09.039573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:09.153270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:09.272704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:09.395254image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:09.513217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:09.635835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:09.760125image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:09.883459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:09.995010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:10.123582image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:10.243370image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-05-25T19:39:10.488693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-05-25T19:39:11.051951image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:11.178944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:11.310800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:11.443599image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:11.574970image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:11.694818image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:11.827322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:11.951664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:12.079280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:12.208110image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:12.398364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:12.537955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-05-25T19:39:12.803235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:12.940525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:13.078213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:13.202691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:13.327493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:13.445645image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:13.563945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:13.682495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:13.806276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:13.932984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:14.054699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:14.191550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:14.320216image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:14.450004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:14.567073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:14.701041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:14.824821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:14.951890image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:15.086944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:15.220072image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:15.355948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:15.486185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:15.624836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:15.761885image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:15.899596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:16.025179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:16.158819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:16.289973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:16.416960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:16.545452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:16.677990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:16.813501image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:16.943399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:17.084364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:17.223708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:17.363779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:17.488612image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:17.621079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:17.745954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:17.872559image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:18.001895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:18.155353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:18.291160image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:18.420714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:18.556555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:18.693395image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:18.828911image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:18.953527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:19.070960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:19.179389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:19.289563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:19.399229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:19.514755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:19.912541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:20.026813image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:20.161258image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:20.282415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:20.403454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2021-05-25T19:39:29.012521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-25T19:39:29.205956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-25T19:39:29.397361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-25T19:39:29.601600image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-25T19:39:29.804070image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-25T19:39:20.703168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:21.309620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:21.669532image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:39:21.920423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

df_indexpropertiesidproperty_typeproperty_statusavailabilitycountrydivisioncitycurrent_zoneszonecentury_zonepriceinterior_areagros_areabedroomsbathroomsother_roomsyear_of_constructionyear_of_renovationclosed_priceagency
00948301ApartmentUsedAvailableAlbaniaTiranaTiranaIsh EkspozitaIsh EkspozitaIsh Ekspozita67525.042.04211.00.01975.00.0NaNcentury
11948275ApartmentUsedAvailableAlbaniaTiranaTiranaOxhakuOxhakuOxhaku59000.084.08421.00.01985.00.0NaNcentury
22948219ApartmentUsedAvailableAlbaniaTiranaTiranaZogu I ZiZogu I ZiZogu I Zi81500.090.09022.00.02019.00.0NaNcentury
33947861ApartmentUsedAvailableAlbaniaTiranaTiranaAstiriAstiriAstiri60000.068.07911.00.02015.00.0NaNcentury
44947596ApartmentUsedAvailableAlbaniaTiranaTiranaInstitut KamëzInstitut KamëzInstitut Kamëz73450.096.011421.00.02015.00.0NaNcentury
55947569ApartmentNewAvailableAlbaniaTiranaTiranaRruga e ElbasanitRruga e ElbasanitRruga e Elbasanit235000.0204.021532.00.02016.00.0NaNcentury
66947536ApartmentUsedAvailableAlbaniaTiranaTiranaInstitut KamëzInstitut KamëzInstitut Kamëz44200.060.06911.00.02015.00.0NaNcentury
77947462ApartmentNewAvailableAlbaniaTiranaTiranaHipotekaHipotekaHipoteka179000.0118.012822.00.02018.00.0NaNcentury
88946773ApartmentUsedIn evaluationAlbaniaTiranaTiranaTregu ElektrikTregu ElektrikTregu Elektrik650000.0404.048633.00.00.00.0NaNcentury
99946757ApartmentUsedAvailableAlbaniaTiranaTiranaStadiumi DinamoStadiumi DinamoStadiumi Dinamo176000.0108.012632.01.01993.00.0NaNcentury

Last rows

df_indexpropertiesidproperty_typeproperty_statusavailabilitycountrydivisioncitycurrent_zoneszonecentury_zonepriceinterior_areagros_areabedroomsbathroomsother_roomsyear_of_constructionyear_of_renovationclosed_priceagency
21704217272660ApartmentNewSoldAlbaniaTiranaTiranaUnaza e reUnaza e reUnaza e Re55000.096.011032.02.00.00.0NaNmei
21705217282659ApartmentUsedWithdrawnAlbaniaTiranaTiranaLiqeni I Tiranes |##| Liqeni i ThateLiqeni I TiranesLiqeni i Tiranës94000.099.09931.02.00.00.0NaNmei
21706217292656ApartmentNewSoldAlbaniaTiranaTiranaDon BoskoDon BoskoDon Bosco98000.0108.011822.01.00.00.0NaNmei
21707217302654ApartmentUsedAvailableAlbaniaTiranaTiranaAli DemiAli DemiAli Demi65000.0120.0040.03.00.00.0NaNmei
21708217312652ApartmentUsedSoldAlbaniaTiranaTiranaDon BoskoDon BoskoDon Bosco55000.072.08031.02.00.00.0NaNmei
21709217322651ApartmentUsedSoldAlbaniaTiranaTiranaBlv. Zogu i PareBlv. Zogu i PareBlv. Zogu i Pare74000.074.08032.02.00.00.0NaNmei
21710217332650ApartmentUsedSoldAlbaniaTiranaTiranaDon BoskoDon BoskoDon Bosco84000.0103.011132.02.00.00.0NaNmei
21711217342649ApartmentUsedSoldAlbaniaTiranaTiranaTirana e ReTirana e ReTirana e Re198000.0138.0042.03.00.00.0NaNmei
21712217352648ApartmentUsedWithdrawnAlbaniaTiranaTiranaTirana e ReTirana e ReTirana e Re95000.0105.011532.02.00.00.0NaNmei
21713217362645ApartmentNewSoldAlbaniaTiranaTiranaKasharKasharKashar22000.048.0011.00.00.00.0NaNmei